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Extensible jacobian and network jacobian interfaces #1634
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #1634 +/- ##
==========================================
+ Coverage 72.80% 72.86% +0.06%
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Files 381 382 +1
Lines 54020 54204 +184
Branches 9210 9238 +28
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+ Hits 39328 39495 +167
- Misses 11707 11714 +7
- Partials 2985 2995 +10 ☔ View full report in Codecov by Sentry. |
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While I don't have enough experience in extensible things for a complete review, I noticed that a lot of test are failing with:
*******************************************************************************
NotImplementedError thrown by MassFlowController::buildReactorJacobian:
Not implemented.
*******************************************************************************
which doesn't appear to be .NET related.
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Before you get any further, I just wanted to suggest undoing the merge commit and rebasing onto the current |
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Thanks for putting this together, @anthony-walker. I think it helps in laying out the necessary pieces for handling the cross-reactor terms in the Jacobian -- in particular, the mechanics surrounding Wall::buildNetworkJacobian
seem like a good basis to keep building on.
I think there is some room for simplification of this structure, which I've left specific comments on. However, I think there is a significant issue with sorting out exactly which derivatives need to be computed and what the right formulas are for the different cases. The challenge I see us needing to resolve to generalize this across the different reactor types is that what derivative is needed for a single interaction (for example, wall heat transfer) depends on both reactor types and what variables they use to represent each term, and we need a way of selecting the correct formula for each Jacobian element based on that pair of reactor types.
// derivative of temperature transformed by ideal gas law | ||
vector<double> moles(m_nsp); | ||
getMoles(moles.data()); | ||
double dTdni = pressure() * m_vol / GasConstant / std::accumulate(moles.begin(), moles.end(), 0.0); |
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Like in IdealGasMoleReactor
,
test/python/test_reactor.py
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# check for values | ||
for i in range(gas1.n_species): | ||
assert np.isclose(jac[0, i + 1 + r1.n_vars], - U * A * gas2.T) | ||
assert np.isclose(jac[r1.n_vars, i + 1], U * A * gas1.T) | ||
if (i + 1 != 2): | ||
assert np.isclose(jac[0, i + 1], U * A * gas1.T, rtol=1e-2) | ||
assert np.isclose(jac[r1.n_vars, i + 1 + r1.n_vars], - U * A * gas2.T, rtol=1e-2) |
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I think you should check against the finite difference Jacobian. This test is just checking against the same incorrect formulas in Python as what's in the C++ implementation.
test/python/test_reactor.py
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gas2 = ct.Solution("h2o2.yaml", "ohmech") | ||
gas2.TPX = 300, ct.one_atm, "O2:1.0" | ||
r2 = self.reactorClass(gas2) | ||
r2.chemistry_enabled = False |
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The analytical Jacobian doesn't currently respect the chemistry_enabled
flag, so there are a lot of non-zero elements here that should be zero based on the setting of this flag.
For an example of where the current derivatives are clearly not correct, consider the following example, adapted from one of your new tests: import cantera as ct
import numpy as np
# create first reactor
gas1 = ct.Solution("h2o2.yaml", "ohmech")
gas1.TPX = 600, ct.one_atm, "O2:1.0"
r1 = ct.IdealGasMoleReactor(gas1)
r1.name = 'R1'
gas1.set_multiplier(0.0)
# create second reactor
gas2 = ct.Solution("h2o2.yaml", "ohmech")
gas2.TPX = 300, ct.one_atm, "O2:1.0"
gas2.set_multiplier(0.0)
r2 = ct.IdealGasMoleReactor(gas2)
r2.name = 'R2'
# create wall
U = 500.0
A = 3.0
w = ct.Wall(r1, r2, U=U, A=A)
net = ct.ReactorNet([r1, r2])
def print_jac(J):
for i,j in zip(*np.nonzero(J)):
name_i = net.component_name(i).replace('temperature', 'T')
name_j = net.component_name(j).replace('temperature', 'T')
net.component_name(i).replace('temperature', 'T')
#print(f"J[{i: 3d}, {j: 3d}] = {J[i,j]:.3e}")
print(f"J[{name_i:8s}, {name_j:8s}] = {J[i,j]:.3e}")
print('Semi-analytical Jacobian:')
print_jac(net.jacobian)
print('\nFinite difference Jacobian:')
print_jac(net.finite_difference_jacobian) which outputs:
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@speth apologies for the delay, I have made some updates but still need to do some of the larger changes. It is still on my radar. |
It can sometimes be useful to add on to the default evaluation in such a way that the standard before or after delegations cannot handle. A default evaluation function allows for this.
in regards to equations, handling volumes, etc.
@speth pushed up some more changes, I will have to put a bit of thought into some of the other changes. |
Changes proposed in this pull request
In this pull request I have setup basic structure for adding contributions to the jacobian from flow devices and walls.
I have also added an extensible jacobian interface to the
ExtensibleReactor
system and modified the existing jacobian system to pass a vector of triplets as is needed in lieu of creating a large number of separate sparse matrices.@speth @ischoegl some additional tests are needed for the wall and flow contributions but I wanted to get the content officially into a PR.
Checklist
scons build
&scons test
) and unit tests address code coverage